Senior Software Engineering Educator - AI Enablement

Mercury is a fintech company seeking a Senior Software Engineering Educator focused on AI Enablement. The role involves helping engineers effectively use AI while maintaining foundational skills, designing training materials, and operationalizing AI-usage guidelines across various experience levels.

Responsibilities:

• Define and operationalize Mercury's AI-usage guidelines for engineering: what engineers should use AI for, what they shouldn't, and how those boundaries shift as skill and context deepen
• Design structured checkpoints and assessment frameworks that detect when AI reliance is accelerating growth versus eroding foundational skills like debugging, code reading, and system reasoning
• Create clear "when to unlatch AI" triggers for onboarding and training—criteria that tell engineers and their managers when someone has built enough foundation to lean more heavily on AI tooling
• Build and iterate on AI-aware training materials that model the right balance: hand-crafted coding where it builds understanding, AI-assisted workflows where it multiplies leverage
• Partner with managers and lead engineers across experience levels to embed AI-usage norms into 1:1s, growth conversations, and performance discussions—not as a separate initiative, but as part of how Mercury engineers develop
• Stand up and evolve a mentorship-focused initiative for software engineers, ensuring mentors model thoughtful AI usage alongside strong engineering craft
• Do the operational work that drives adoption: scheduling, facilitation, follow-ups, and iteration based on feedback
• Collaborate closely with training team members and cross-functional partners to drive broader skill acquisition efforts

Requirements:

• Has 5+ years of shipping quality software into production while mentoring peer software engineers in a start-up environment
• Has hands-on experience with AI coding tools and a thoughtful, opinionated perspective on where they help and where they hinder
• Communicates clearly and gives actionable, direct, kind feedback
• Enjoys turning fuzzy goals into simple, repeatable programs
• Knows when to lean into 1:1 sessions or organizational legwork to drive adoption or improve learning outcomes
• Models a care of craftsmanship and healthy engineering habits—including deliberate, principled AI usage rather than reflexive reliance
• Loves turning passive, explanatory content into active, exercise-centric learning resources
• Is energized by the tension between productivity and skill development, and sees designing for both as a core challenge rather than a tradeoff to accept
• Haskell experience or strong willingness to learn Mercury's primary stack is a plus